• DocumentCode
    13682
  • Title

    Cross-community sensing and mining

  • Author

    Bin Guo ; Zhiwen Yu ; Daqing Zhang ; Xingshe Zhou

  • Author_Institution
    Sch. of Comput. Sci., Northwestern Polytech. Univ., Xi´an, China
  • Volume
    52
  • Issue
    8
  • fYear
    2014
  • fDate
    Aug. 2014
  • Firstpage
    144
  • Lastpage
    152
  • Abstract
    With the developments in information and communications technology (ICT), people are involving in and connecting via various forms of communities in the cyber-physical space, such as online communities, opportunistic (offline) social networks, and location-based social networks. Different communities have distinct features and strengths. With humans playing the bridge role, these communities are implicitly interlinked. In contrast with the existing studies that mostly consider a single community, this article addresses the interaction among distinct communities. In particular, we present an emerging research area - cross-community sensing and mining (CSM), which aims to connect heterogeneous, cross-space communities by revealing the complex linkage and interplay among their properties and identifying human behavior patterns by analyzing the data sensed/collected from multi-community environments. The article describes and discusses the research background, characters, general framework, research challenges, as well as our practice of CSM.
  • Keywords
    data mining; social networking (online); CSM; complex linkage; cross-community sensing and mining; cross-space communities; human behavior pattern identification; Cellular phones; Data mining; Information technology; Internet; Mobile communication; Social network services;
  • fLanguage
    English
  • Journal_Title
    Communications Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    0163-6804
  • Type

    jour

  • DOI
    10.1109/MCOM.2014.6871682
  • Filename
    6871682